Multisolvent metabolite profiling of coffee waste by UHPLC-HRMS/MS and molecular networking

Area of Science:

• Agricultural Chemistry
• Food Chemistry
• Biotechnology

Background:

• Coffee processing generates substantial waste, including defected green beans (GB) and spent coffee grounds (SCG).
• These by-products are rich in bioactive compounds with potential applications in nutraceuticals and cosmetics.
• Current utilization of these wastes remains limited, representing an underutilized resource.

Purpose of the Study:

• To comprehensively profile metabolites from Arabica and Robusta coffee wastes (GB and SCG).
• To investigate the impact of solvent polarity on metabolite extraction efficiency.
• To establish a framework for guiding the valorization of coffee by-products.

Main Methods:

• Metabolite profiling using UHPLC-HRMS/MS and GC-MS.
• Quantification of chlorogenic acid and caffeine via HPLC-DAD.
• Chemometric analyses including Principal Coordinate Analysis (PCoA) and Random Forest (RF) modeling.
• Molecular networking (MN) for visualizing metabolite clusters.

Main Results:

• Solvent polarity dictates the type of compounds extracted; non-polar solvents recover fatty acids and sterols (especially from SCG), while polar solvents like ethanol extract hydrophilic antioxidants (e.g., chlorogenic acid from GB).
• Significant variations in metabolite profiles were observed based on solvent polarity and coffee bean type (Arabica vs. Robusta).
• Molecular networking effectively visualized metabolite distributions influenced by extraction conditions and material origin.

Conclusions:

• Extraction method and raw material origin are critical determinants of metabolite diversity and functional potential in coffee waste.
• The integrated approach using molecular networking, multivariate statistics, and machine learning provides a robust strategy for chemical mapping of coffee by-products.
• Further bioactivity validation is necessary to realize specific applications for valorized coffee wastes.